AI-POWERED LEARNING THROUGH HUMAN-COMPUTER INTERACTION: THE ROOT SYSTEM MODEL

Abstract

Artificial Intelligence (AI) is rapidly reshaping the educational landscape by enabling faster, deeper and more personalized learning experiences. This paper introduces the Root System model— a conceptual framework in which AI, supported by responsive Human-Computer Interaction (HCI), guides learners through a branching, tree-like structure of knowledge acquisition. Mirroring a root system, learners begin with central concepts (roots) that expand into related subtopics (lateral roots) and further into detailed sub-concepts (tertiary roots) through recursive questioning and real-time feedback. This interactive, non-linear exploration fosters a zero-doubt learning environment where learners proceed only after achieving full conceptual clarity. The study employs a conceptual and comparative methodology, drawing from educational psychology, constructive learning theory and AI-driven HCI systems. A hypothetical implementation of the Root System in digital learning platforms is also presented. Findings suggest that Root System learning significantly reduces time to comprehension, boosts learner motivation and cultivates independent inquiry and metacognition. It also preserves content authenticity by enabling AI to draw explanations exclusively from trusted academic sources such as textbooks, thus minimizing misinformation. The paper concludes that integrating the Root System into educational ecosystems transforms AI from a passive tool into a dynamic, ethical and curiosity-driven learning companion. Strategic recommendations are provided for educational developers and policymakers to guide their effective adoption.